Decorate functions using macros in Elixir

After I decided to make public a telegram bot to monitor bus time in Dublin (@dublin_bus_bot). Before the release I became curious to see how many people will use it (spoiler: just an handful) and I thought that would be a good idea to track the use on google analytics.

Overview

Google analytics provide a measurement protocol that can be used to track things that are different from websites (mobile apps, IOT). At the moment no elixir client exists for this protocol (and it would not be anything more than an api wrapper). My plan is to make call to the Google Analytics TK endpoint with HTTPOison but I’d prefer to not have to call the tracking function for every single bot command.

One of the feature that I prefer of the elixir are macros, macros allow to generate code at compile time. I decided to define a macro that looking like a function definition would define a function with the same body and with an additional call to the track function. I decided this approach because seems more idiomatics than using the decorator syntax typical of other languages (@decorator at least in python and javascript).

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defmetered sample_function(arg1, arg2) do
IO.inspect([arg1, arg2])
end
# would generate something similar to
def sample_function(arg1, arg2) do
track(:sample_function, [arg1: arg1, arg2: arg2])
IO.inspect([arg1, arg2])
end

Implementation

I implemented this approach in meter to use in the telegram bot I wrote.

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@doc """
Replace a function definition, automatically tracking every call to the function
on google analytics. It also track exception with the function track_error.
This macro intended use is with a set of uniform functions that can be concettualy
mapped to pageviews (eg: messaging bot commands).
Example:
defmetered function(arg1, arg2), do: IO.inspect({arg1,arg2})
function(1,2)
will call track with this parameters
track(:function, [arg1: 1, arg2: 2])
Additional parameters will be loaded from the configurationd
"""
# A macro definition can use pattern matching to destructure the arguments
defmacro defmetered({function,_,args} = fundef, [do: body]) do
# arguments are defined in 3 elements tuples
# this extract the arguments names in a list
names = Enum.map(args, &elem(&1, 0))
# meter will contain the body of the function that will be defined by the macro
metered = quote do
# quote and unquote allow to switch context,
# simplyfing a lot quoted code will run when the function is called
# unquoted code run at compile time (when the macro is called)
values = unquote(
args
|> Enum.map(fn arg -> quote do
# allow to access a value at runtime knowing the name
# elixir macros are hygienic so it's necessary to mark it
# explicitly
var!(unquote(arg))
end
end)
)
# Match argument names with their own values at call time
map = Enum.zip(unquote(names), values)
# wrap the original function call with a try to track errors too
try do
to_return = unquote(body)
track(unquote(function), map)
to_return
rescue
e ->
track_error(unquote(function), map, e)
raise e
end
end
# define a function with the same name and arguments and with the augmented body
quote do
def(unquote(fundef),unquote([do: metered]))
end
end

Conclusions

Elixir macros are a powerful tool to abstract away some functionality or to write DSLs. They require a bit of time to wrap head around, in particular with the context swith, but it totally worth the hassle if you can reduce the clutter in your code base.

Serverless Telegram Bot on GC Functions

I played for some time with the idea of having a telegram bot run serverless in the cloud. Obviously the code run on some server but it is not necessary to care to provision, deploy, starting the application, etc. All you care about is your code.

GC Functions can be triggered by Pub/Sub events, buckets events and HTTP invocations. The latter is the one that we are going to provide as webhook to Telegram to be invoked when a message is sent to our bot.

Functions are going to remove some friction from our code, when the request is set with the appropriate application/json header the parsed json will be available on the request and when we send back an object is automatically serialized and sent back to the client.

The example code of the project can be found at https://github.com/carlo-colombo/serverless-telegram-bot-gc-functions

Prerequisites

Warning

  • Both Google Cloud Functions and RuntimeConfig are both still in beta.
  • Even if the GCP free tier is quite extended some costs can be billed.

The token

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# export for local testing
export TELEGRAM_TOKEN=133545asdasd
# set the token as GC runtime configuration
gcloud beta runtime-config configs create prod-config
gcloud beta runtime-config configs variables \
set telegram/token "$TELEGRAM_TOKEN" \
--config-name prod-config

The bot

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exports.echoBot = function(req, res){
const {message:{chat, text}} = req.body
const echo = `echo: ${text}`
return getToken()
.then( token => request.post({
uri: `https://api.telegram.org/bot${token}/sendMessage`,
json: true,
body: {text: echo, chat_id: chat.id}
}))
.then(resp => res.send(resp))
.catch(err => res.status(500).send(err))
}

Just an easy bot that echos the received message.

Retrieving the token

This function return a (promise) of a token either from the runtime config api when run online or from an environment variable when run locally. The value is retrieved using fredriks/cloud-functions-runtime-config that wraps the api. NODE_ENV is set to production when the function is run online, thus allowing to discriminate in which environment the function run.

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function getToken(){
if (process.env.NODE_ENV == 'production'){
return require('cloud-functions-runtime-config')
.getVariable('prod-config', 'telegram/token')
}
return Promise.resolve(process.env.TELEGRAM_TOKEN)
}

Local testing

Google provide a local emulator for Functions feature. It allow to local deploy a function to iterate over it without having to deploy to the google server. It reload the code when changed on the file system so it is not necessary to redeploy after the first time.

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npm -g install @google-cloud/functions-emulator
functions start
functions deploy echoBot --trigger-http
curl -X POST \
-H "Content-Type: application/json" \
-d '{
"message": {
"chat": {
"id": 1232456
},
"text": "hello world"
}
}' \
http://localhost:8010/PROJECT_ID/us-central1/echoBot
# To tail logs
watch functions logs read

Deploying

Before deploy the function is required to create a Cloud Storage bucket where the function will be stored

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gsutil mb -c regional -l us-central1 gs://unique-bucket-name
gcloud beta functions deploy function_name \
--trigger-http \
--entry-point echoBot \
--stage-bucket unique-bucket-name

Set up the webhook

Deploying the function with the http trigger will return an url to trigger the function. The url would look like https://<GCP_REGION>-<PROJECT_ID>.cloudfunctions.net/function_name. Use this url to set up a web hook for your bot on telegram. You can check more information on webhook on the Telegram API documentation

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curl -X POST \
-H "Content-Type: application/json" \
-d '{
"url": "https://<GCP_REGION>-<PROJECT_ID>.cloudfunctions.net/function_name"
}' \
https://api.telegram.org/bot${TELEGRAM_TOKEN}/setWebhook

Conclusions

Setting up a Telegram bot using Google Cloud Functions is quick and easy, and with the HTTP trigger is possible to seamlessy set a webhook endpoint for a bot without having to care about a server and https certificates (http trigger are https).

One last thing to keep in mind is that Functions are stateless and require to be connected to other services to store data or be for example scheduled.